Holger Stenzhorn

BioTop: An upper domain ontology for the life sciences: A description of its current structure, contents and interfaces to OBO ontologies

In the life sciences, there is an ample need for semantic interoperability of data. Thus shared vocabularies are needed for consistently expressing metadata in terms of semantic annotations as well as for querying bibliographic information systems. In the past years, lots of highly specialized, yet also fragmented terminologies have evolved. However, they lack principled forms of conceptual interlinkage. In order to provide an ontological basis for a seamless integration of such isolated parts of biological knowledge, we here introduce BioTop, an upper domain ontology for molecular biology. We describe its structure and contents, as well as its current interfaces to a selected set of OBO ontologies, which contain more detailed terminological knowledge about specific areas of molecular biology, e.g., cell types, molecular functions, biological processes and chemical compounds.

Semantic sitemaps: efficient and flexible access to datasets on the semantic web

Increasing amounts of RDF data are available on the Web for consumption by Semantic Web browsers and indexing by Semantic Web search engines. Current Semantic Web publishing practices, however, do not directly support efficient discovery and high-performance retrieval by clients and search engines. We propose an extension to the Sitemaps protocol which provides a simple and effective solution: Data publishers create Semantic Sitemaps to announce and describe their data so that clients can choose the most appropriate access method. We show how this protocol enables an extended notion of authoritative information across different access methods.

The ACGT Master Ontology and its applications - Towards an ontology-driven cancer research and management system

OBJECTIVE This paper introduces the objectives, methods and results of ontology development in the EU co-funded project Advancing Clinico-genomic Trials on Cancer-Open Grid Services for Improving Medical Knowledge Discovery (ACGT). While the available data in the life sciences has recently grown both in amount and quality, the full exploitation of it is being hindered by the use of different underlying technologies, coding systems, category schemes and reporting methods on the part of different research groups. The goal of the ACGT project is to contribute to the resolution of these problems by developing an ontology-driven, semantic grid services infrastructure that will enable efficient execution of discovery-driven scientific workflows in the context of multi-centric, post-genomic clinical trials. The focus of the present paper is the ACGT Master Ontology (MO). METHODS ACGT project researchers undertook a systematic review of existing domain and upper-level ontologies, as well as of existing ontology design software, implementation methods, and end-user interfaces. This included the careful study of best practices, design principles and evaluation methods for ontology design, maintenance, implementation, and versioning, as well as for use on the part of domain experts and clinicians. RESULTS To date, the results of the ACGT project include (i) the development of a master ontology (the ACGT-MO) based on clearly defined principles of ontology development and evaluation; (ii) the development of a technical infrastructure (the ACGT Platform) that implements the ACGT-MO utilizing independent tools, components and resources that have been developed based on open architectural standards, and which includes an application updating and evolving the ontology efficiently in response to end-user needs; and (iii) the development of an Ontology-based Trial Management Application (ObTiMA) that integrates the ACGT-MO into the design process of clinical trials in order to guarantee automatic semantic integration without the need to perform a separate mapping process.

The ontology of biological taxa

Motivation: The classification of biological entities in terms of species and taxa is an important endeavor in biology. Although a large amount of statements encoded in current biomedical ontologies is taxon-dependent there is no obvious or standard way for introducing taxon information into an integrative ontology architecture, supposedly because of ongoing controversies about the ontological nature of species and taxa. Results: In this article, we discuss different approaches on how to represent biological taxa using existing standards for biomedical ontologies such as the description logic OWL DL and the Open Biomedical Ontologies Relation Ontology. We demonstrate how hidden ambiguities of the species concept can be dealt with and existing controversies can be overcome. A novel approach is to envisage taxon information as qualities that inhere in biological organisms, organism parts and populations. Availability: The presented methodology has been implemented in the domain top-level ontology BioTop, openly accessible at http://purl.org/biotop. BioTop may help to improve the logical and ontological rigor of biomedical ontologies and further provides a clear architectural principle to deal with biological taxa information. Contact: stschulz@uni-freiburg.de

The ACGT Master Ontology on Cancer A New Terminology Source for Oncological Practice

We present a new source of terminology for transnational data exchange in oncology, emphasizing the integration of both clinical and molecular data. In order to achieve best results in semantic interoperability, the ACGT project provides an ontology on cancer research and management. Besides examining pre-existing sources of terminology, were view methods of ontology development, and present best practices to be employed in the development of the ACGT Master Ontology. The clinical trial management system that is currently developed within ACGT constitutes a central use of the ontology at this point.

Granularity Issues in the Alignment of Upper Ontologies

OBJECTIVES The application of upper ontologies has been repeatedly advocated for to support the interoperability between different domain ontologies for facilitating the shared use of data within and across disciplines. BioTop is an upper domain ontology that aims at aligning more specialized biomolecular and biomedical ontologies. The integration of BioTop and the upper ontology Basic Formal Ontology (BFO) is the objective of this study. METHODS BFO was manually integrated into BioTop, observing both its free text and formal definitions. BioTop classes were attached to BFO classes as children and BFO classes were reused in the formal definitions of BioTop classes. A description logics reasoner was used to check the logical consistency of this integration. The domain adequacy was checked manually by domain experts. RESULTS Logical inconsistencies were found by the reasoner when applying the BFO classes for fiat and aggregated objects in some of the BioTop class definitions. We discovered that the definition of those particular classes in BFO was dependent on the notion of physical connectedness. Hence we suggest ignoring a BFO subbranch in order not to hinder cross-granularity integration. CONCLUSION Without introducing a more sophisticated theory of granularity, the described problems cannot be properly dealt with. Whereas we argue that an upper ontology should be granularity-independent, we illustrate how granularity-dependent domain ontologies can still be embedded into the framework of BioTop in combination with BFO.

Establishing a distributed system for the simple representation and integration of diverse scientific assertions

BackgroundInformation technology has the potential to increase the pace of scientific progress by helping researchers in formulating, publishing and finding information. There are numerous projects that employ ontologies and Semantic Web technologies towards this goal. However, the number of applications that have found widespread use among biomedical researchers is still surprisingly small. In this paper we present the aTag (‘associative tags’) convention, which aims to drastically lower the entry barriers to the biomedical Semantic Web. aTags are short snippets of HTML+RDFa with embedded RDF/OWL based on the Semantically Interlinked Online Communities (SIOC) vocabulary and domain ontologies and taxonomies, such as the Open Biomedical Ontologies and DBpedia. The structure of aTags is very simple: a short piece of human-readable text that is ‘tagged’ with relevant ontological entities. This paper describes our efforts for seeding the creation of a viable ecosystem of datasets, tools and services around aTags.ResultsNumerous biomedical datasets in aTag format and systems for the creation of aTags have been set-up and are described in this paper. Prototypes of some of these systems are accessible at http://hcls.deri.org/atagConclusionsThe aTags convention enables the rapid development of diverse, integrated datasets and semantically interoperable applications. More work needs to be done to study the practicability of this approach in different use-case scenarios, and to encourage uptake of the convention by other groups.

Applications of the ACGT Master Ontology on Cancer

In this paper we present applications of the ACGT Master Ontology (MO) which is a new terminology resource for a transnational network providing data exchange in oncology, emphasizing the integration of both clinical and molecular data. The development of a new ontology was necessary due to problems with existing biomedical ontologies in oncology. The ACGT MO is a test case for the application of best practices in ontology development. This paper provides an overview of the application of the ontology within the ACGT project thus far.

Analysis of the Suitability of Existing Medical Ontologies for Building a Scalable Semantic Interoperability Solution Supporting Multi-site Collaboration in Oncology

Semantic interoperability is essential to facilitate efficient collaboration in heterogeneous multi-site healthcare environments. The deployment of a semantic interoperability solution has the potential to enable a wide range of informatics supported applications in clinical care and research both within as ingle healthcare organization and in a network of organizations. At the same time, building and deploying a semantic interoperability solution may require significant effort to carryout data transformation and to harmonize the semantics of the information in the different systems. Our approach to semantic interoperability leverages existing healthcare standards and ontologies, focusing first on specific clinical domains and key applications, and gradually expanding the solution when needed. An important objective of this work is to create a semantic link between clinical research and care environments to enable applications such as streamlining the execution of multi-centric clinical trials, including the identification of eligible patients for the trials. This paper presents an analysis of the suitability of several widely-used medical ontologies in the clinical domain: SNOMED-CT, LOINC, MedDRA, to capture the semantics of the clinical trial eligibility criteria, of the clinical trial data (e.g., Clinical Report Forms), and of the corresponding patient record data that would enable the automatic identification of eligible patients. Next to the coverage provided by the ontologies we evaluate and compare the sizes of the sets of relevant concepts and their relative frequency to estimate the cost of data transformation, of building the necessary semantic mappings, and of extending the solution to new domains. This analysis shows that our approach is both feasible and scalable.

Simplifying access to large-scale health care and life sciences datasets

Within the health care and life sciences (HCLS) domain, a plethora of phenomena exists that range across the whole “vertical scale” of biomedicine. To accurately research and describe those phenomena, a tremendous amount of highly heterogeneous data have been produced and collected with various research methodologies encompassing the genetic, molecular, tissue, and organ level. An initial step to provide researchers with access to this data has been through creating integrated views on existing and open biomedical datasets published on the Web. In order to make the next step, we need to now create easy-to-use yet powerful applications that enable researchers to efficiently query, integrate and analyze those datasets.